With the rapid development of functional MRI (fMRI) in neuroscience and the increasing number of studies using fMRI in clinical research, there is a need for automated methods to analyze and interpret the data. The high sensitivity of fMRI enables online analysis, which calls for online interpretation of the results in terms of underlying sensory, motor and cognitive processes. This capability can help the user to maximize the information obtained from fMRI and to make decisions on the data quality. The goals are to substantially improve the performance of multi-class pattern classification of very high- dimensional fMRI data with limited number of sample data sets, and to develop an integrated tool based on our custom- designed real-time fMRI analysis platform (TurboFIRE) to perform pattern classification of dynamically changing activation patterns during an ongoing real-time fMRI scan. The aims are (a) Develop an integrated high-performance fMRI analysis chain for real-time pattern recognition, (b) Develop novel sparsity-adaptive aggregation and PLS methods for pattern classification, and (c) Characterize the performance of the data analysis chain for classifying spatially distributed activation patterns by demonstrating the methods on motor, visual, auditory and mental computation tasks. The successful demonstration of this automatic, real-time functional MRI methodology will provide a proof-of-concept of classifying dynamically changing brain activation patterns during the ongoing scan. This will provide a criterion for successfully performing a sensory and motor function in the clinical setting and facilitate the identification of higher cognitive processes in the research setting. Online display of the classification result will also assist the user in adapting the paradigm to improve specificity and enables an interactive interview of the subject to further explore underlying brain processes. The long-term goal of this project is to develop an automated fMRI analysis tool that will have significant commercial potential for clinical and research use. [unreadable] [unreadable] [unreadable]